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This publication gathers the conscientiously reviewed complaints of the nineteenth foreign convention on structures technological know-how, offering fresh learn findings within the parts of synthetic Intelligence, desktop studying, Communication/Networking and data expertise, keep watch over thought, selection aid, snapshot Processing and machine imaginative and prescient, Optimization ideas, development reputation, Robotics, carrier technology, Web-based companies, doubtful platforms and Transportation structures. The overseas convention on platforms technology used to be held in Wroclaw, Poland from September 7 to nine, 2016, and addressed a number themes, together with structures concept, keep watch over thought, computing device studying, synthetic intelligence, sign processing, conversation and data applied sciences, transportation structures, multi-robotic structures and unsure structures, in addition to their purposes. the purpose of the convention is to supply a platform for conversation among younger and confirmed researchers and practitioners, fostering destiny joint study in structures science.

This booklet constitutes the refereed complaints of the fifteenth overseas convention on Web-Based studying, ICWL 2016, held in Rome, Italy, in October 2016. the nineteen revised complete papers offered including 10 brief papers and four poster papers have been conscientiously reviewed and chosen from a hundred and ten submissions.

This quantity within the Academy of overseas enterprise Latin the United States bankruptcy (AIB-LAT) sequence offers learn findings and theoretical advancements in foreign enterprise, with particular emphasis on innovation, geography and internationalization in Latin the USA. Contributions are in response to the easiest papers from the fourth annual AIB-LAT convention.

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Wnew ¼ wj Ã ð1 þ bÞ j ð2Þ The process of decreasement of model weight is deﬁned by Eq. (3). wnew ¼ wj Ã b j ð3Þ Then the method normalizes the models weights to an interval <0, 1> by subprocedure ‘Normalize_Experts_Weights’. Subsequently the global prediction of the whole ensemble is computed by Eq. (4). ^fglobal ¼ Á Pn À ^ i¼1 wi Ã fi Pn i¼1 ðwi Þ ð4Þ where n is the number of prediction models in ensemble, wi is the assigned weight of ith model and ^fi is the prediction of ith model. If the predeﬁned number of iterations was reached or the prediction error of the ensemble is higher than the speciﬁed threshold (parameter h), the method proceeds into the removal phase, where the “weak” models are removed and replaced by new created models (line 18).

2. MAPE of AddExp and DWM methods optimized by PSO Prediction of Power Load Demand 45 4 Evaluation In this chapter we focus on comparison of errors between the predictions of three modiﬁcations of original DWM method and six base predictive models. Before we describe the designed experiments and results, we focus on electricity loads dataset used in our study. 2015. These records were obtained by smart meters that send information about the actual electricity consumption every 15 min. Our dataset consists of ca.